Biology subjects

Wang, H. R.

Publications and source records attributed to Wang, H. R..

3 recordsLinked to original sources

bioRxiv2025

Multivariate Resting-State Functional Connectivity Features Linked to Transdiagnostic Psychopathology in Early Psychosis

BackgroundEarly psychosis (EP) is characterized by neurobiological changes, including alterations in resting-state functional connectivity (RSFC). We now understand that symptoms and neural changes may overlap across EP diagnostic categories. However, the relationship between RSFC patterns and transdiagnostic symptom dimensions remains poorly understood. MethodsWe employed Partial Least Squares correlation to examine multivariate relationships between whole-brain RSFC and clinical symptoms in 124 EP patients (aged 16-35 years) diagnosed with schizophrenia, schizoaffective disorder, or a psychotic mood disorder. RSFC was computed among 216 cortical and subcortical regions. Clinical assessment included 41 symptom measures spanning positive, negative, general psychopathology, and manic dimensions. ResultsAnalysis revealed one significant latent component (p<0.001) capturing 41.6% of the RSFC-symptom covariance. This component was characterized by increased between-network connectivity, particularly involving sensory-motor, default mode, and subcortical regions including the amygdala and thalamus. The associated symptom profile included cognitive rigidity and arousal dysregulation (stereotyped thinking, anxiety, and somatic concerns), rather than traditional positive or negative symptoms. This brain-behavior relationship was consistent across diagnoses and independent of medication and substance use. The clinical relevance was validated through significant correlations with standardized measures of hostility (r=0.23), negative affect (r=0.25), and perceived stress (r=0.22). ConclusionsOur findings reveal a distinct transdiagnostic phenotype in EP characterized by cognitive inflexibility and arousal dysregulation that is associated with altered integration between sensory, default mode, and subcortical networks. This work suggests that specific patterns of network-level functional connectivity may relate to symptom dimensions that cut across conventional diagnostic boundaries, potentially informing more targeted therapeutic approaches.

neuroscience↗

bioRxiv2025

Childhood Maltreatment and Deviations from Normative Brain Structure: Results from 3,711 Individuals from the ENIGMA MDD and ENIGMA PTSD

Childhood maltreatment (CM), encompassing abuse and neglect, affects over two-thirds of the general population and increases risk for stress-related psychopathology, including major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). The extent to which neuroanatomical alterations in MDD and PTSD are attributable to CM, however, is uncertain. Here, we analyzed CM and 3D structural brain MRI data from 3,711 participants in the ENIGMA MDD and PTSD Working Groups (25 sites; 33.3{+/-}13.0 years; 59.9% female). Normative modeling estimated deviation z-scores for 14 subcortical volumes (SV), 68 cortical thickness (CT), and 68 surface area (SA) measures, capturing differences from population norms. Transdiagnostic associations between CM and brain deviation scores were evaluated within each sex and age cohort. In young adults (ages 18-35), abuse was associated with larger volumes in thalamus and pallidum, thinner isthmus cingulate and middle frontal regions, and thicker medial orbitofrontal cortex; there were no significant effects in pediatric ([≤]18 years) participants. The strongest effects were observed in young female adults (|{beta}|=.07-.22, q<.05): greater abuse and neglect were correlated with smaller hippocampus and putamen volumes, thinner entorhinal cortex, and smaller SA in fusiform/inferior parietal regions, and with larger SA in orbitofrontal and occipital cortices. In males, abuse had widespread effects on CT and SA (|{beta}|=.1-.18, q<.05); effects for neglect were minimal. Our findings of age- and sex-specific instantiations of CM on brain morphometry highlight the importance of developmental context in understanding how adverse experiences shape neurobiological vulnerability to MDD and PTSD.

neuroscience↗

bioRxiv2024

Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated with Psychopathology Across Independent Cohorts

BackgroundEarly Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions. MethodsA data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. ResultsIn both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use. ConclusionsThis data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

neuroscience↗